1. Field of the Invention
The invention relates to the field of mobile communications, and more particularly to the field of locating a particular mobile device on a mobile telecommunications network.
2. Discussion of the State of the Art
For a mobile telephone system to work, the appropriate transmitter tower (or base transceiver station, “BTS”, also known commonly as a “base station” in the art), must transmit the telephone call to the user's device (which may be a mobile phone, tablet computing device, or any of a variety of electronic devices designed to communicate via a wireless radio network). Mobile phone systems therefore continuously track the “cell” a device is currently in. This basic, required knowledge of the location of a handset is limited to the “Cell ID”, or an identifier of a specific transmitter to which a device is connected, and simply determines the geographical area served by that transmitter.
However, the location of a mobile device is also of use in other ways: for example, emergency services may need to be able to locate the position of a caller when responding to a call for help; law enforcement or intelligence agencies may wish to track the location of a suspected criminal or terrorist; or location-based commercial services may desire to know a precise location of a mobile phone (and its user) in order to target specific services to the user based on her location. Increasingly, a number of commercial services also rely on, or are enhanced by, knowing the location of a mobile device. For example, these may allow individuals to locate each other, to find nearby businesses or services etc. and thereby increase the value of any such device or service provider.
As the geographical extent of a mobile phone cell can encompass many square kilometers, a more precise estimate of the device's location may be required for the optimum provision of such location-based services (“LBS”). LBS location algorithms can be split in two main categories, as described below.
Predictive Methods:
the goal is to predict the distance from one or more BTS to a mobile device using received signal information and then calculate the position of the device through triangulation (or, more generally, multilateration) or simple calculation.
The strength of a signal from one or more BTS may be available (and from this, along with the antenna orientation and propagation pattern, the distance, and angle from any particular cell may be inferred), though this is prone to error when the paths to different base stations have different signal loss characteristics. If the path to one base station is clear line of sight while that to another is obstructed by trees and that to a third is through a built-up area, then the loss characteristics of these paths vary, making direct comparison of signal strength problematic.
There may also be timing information available that allows distance to a base station to be inferred from the time taken for the signal to pass between handset and base station. The simplest approach to this uses the Timing Advance parameter that is an integral part of Global System for Mobile Communication (“GSM”) networks. To ensure accurate timing of the signals as received at a base station, a mobile device will adjust the time at which it starts to transmit in its allocated timing slot by between zero and 63 times 3.69 microseconds. Each such delay compensates for a round-trip of 1100 meters and hence corresponds to an additional 550 meters between handset and BTS.
Other technologies such as Uplink Time Difference of Arrival (“U-TDOA”) utilize more precise timing information instead of signal levels but require location measurement units (“LMUs”) to be added throughout the mobile network infrastructure. These measure the timing of received signals more precisely than the inherent GSM Timing Advance accuracy, thus allowing a more accurate determination of a device's position. These techniques provide what is known as Enhanced Cell ID (“ECID”).
Predictive methods such as ECID usually lead to poor results in urban areas because the prediction easily becomes unreliable due to reflections and multipath propagation effects. In other words, the predicted signal level does not accurately match the observed values. This method is, however, very simple to implement and leads to more acceptable results in rural environments and open areas where both the prediction is more reliable and the need for accuracy is somewhat reduced for most commercial and non-commercial applications. Accuracy may also suffer due to changes between the time a fingerprint was stored and the current time in which a location is being determined, such as new construction or landscaping in an urban area, or variations in signal caused by electromagnetic interference as may be common in developed areas with a high density of electronic devices.
U-TDOA methods, by contrast, offer high accuracy even in urban areas but suffer from a high implementation cost due to the deployment of LMUs to all BTS and the necessary associated maintenance costs. The accuracy is also affected by reflections and propagation effects, although less so than power based methods.
RF Fingerprinting:
this approach is to take data points on the ground with a Global Positioning System (“GPS”) enabled device and build a database of “fingerprints”, each of which specifies a location and the signal strength of the various transmitters that can be received at that location. A matching algorithm is then used to find the closest fingerprint and deduce the position of the handset.
Radio frequency (“RF”) fingerprinting methods are based on the same radio information as ECID (e.g. cell id, timing advance, signal level (GSM), or similar in other mobile networks such as Code Division Multiple Access (CDMA) and 3G networks), but rely on a large number of data points being collected at short intervals (known as “heavy drive testing” since these are normally obtained by driving a car equipped with the appropriate measurement equipment to each point) to build an adequate database of data points. This approach gives good accuracy in urban environments and avoids the need for hardware deployment on the radio network. The cost of ownership is, however, significant because of the need for initial and ongoing drive testing and updating of the fingerprint database as new transmitters are added, as buildings go up or vegetation grows or is cut down.
What is needed is a solution that provides the high accuracy of these two approaches without the heavy cost of hardware (in the case of U-TDOA) or of extensive drive testing (in the case of RF fingerprinting).